Experiment
Yeast as background and USP2 proteins spiked in different concentration. This Experiment is designed for DIA benchmarking of different workflow using DIA-NN.
Import data
Reading DIA data from data/report.tsv using Precursor.Translated intensities from DIA-NN.
All the precursors with Q-value < 0.01 are used for the analysis.
Filtering steps
The pre-processing steps are:
- Filtering precursor with more than 3 samples with no missing intensities across all the samples
- Select only proteotypic peptides
- Select proteins with at least 3 peptides
- Contaminats proteins are not filtered
Data Missing Analysis
The plot shows the completeness of the experiments at precursor and summarized level. On the the x-axis peptides/proteins are ordered based on the increasing NA content.
Normalization
Raw intensities from DIA-NN are processed as follow:
- Log 2 transformation
- Normalized across all the samples using quantiles method
- Summarization at protein level using medianPolish function
PCA
DE Analysis
Using MSqRob2 with the following formula ~ -1 + Group with ridge regression disabled
GroupA GroupB GroupC GroupD GroupE
14.98936 14.77959 14.88881 15.01346 14.95730 QC plots
Group Comparison
This volcano plot summarizes the differential expression landscape in the comparison between the two groups
These bar plots summarizes the number of significantly upregulated/downregulated number of proteins based on different adjusted p-values (selected adjusted p-values are 0.001, 0.01, 0.05, and 0.1 - see facet headers) and log2 fold-change thresholds (on the x-axis) used to define the significance levels.
This volcano plot summarizes the differential expression landscape in the comparison between the two groups
These bar plots summarizes the number of significantly upregulated/downregulated number of proteins based on different adjusted p-values (selected adjusted p-values are 0.001, 0.01, 0.05, and 0.1 - see facet headers) and log2 fold-change thresholds (on the x-axis) used to define the significance levels.
Summary of the DE proteins
In the table you can find a summaries of the number of DE proteins found in all the comparisons. The overlapping of DE proteins among the comparisons is also showed using an upset plot.
Session Info
R version 4.3.3 (2024-02-29 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Belgium.utf8 LC_CTYPE=English_Belgium.utf8
[3] LC_MONETARY=English_Belgium.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Belgium.utf8
time zone: Europe/Brussels
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] reshape2_1.4.4 heatmaply_1.5.0
[3] viridis_0.6.5 viridisLite_0.4.2
[5] pheatmap_1.0.12 UpSetR_1.4.0
[7] gridExtra_2.3 hrbrthemes_0.8.7
[9] factoextra_1.0.7 MSnbase_2.28.1
[11] ProtGenerics_1.34.0 mzR_2.36.0
[13] Rcpp_1.0.12 DT_0.33
[15] plotly_4.10.4 here_1.0.1
[17] magrittr_2.0.3 lubridate_1.9.3
[19] forcats_1.0.0 stringr_1.5.1
[21] purrr_1.0.2 readr_2.1.5
[23] tidyr_1.3.1 tibble_3.2.1
[25] tidyverse_2.0.0 dplyr_1.1.4
[27] ggrepel_0.9.5 ggplot2_3.5.1
[29] visdat_0.6.0 readxl_1.4.3
[31] msqrob2_1.10.0 QFeatures_1.12.0
[33] MultiAssayExperiment_1.28.0 SummarizedExperiment_1.32.0
[35] Biobase_2.62.0 GenomicRanges_1.54.1
[37] GenomeInfoDb_1.38.8 IRanges_2.36.0
[39] S4Vectors_0.40.2 BiocGenerics_0.48.1
[41] MatrixGenerics_1.14.0 matrixStats_1.3.0
loaded via a namespace (and not attached):
[1] splines_4.3.3 later_1.3.2 bitops_1.0-7
[4] cellranger_1.1.0 preprocessCore_1.64.0 XML_3.99-0.16.1
[7] lifecycle_1.0.4 rstatix_0.7.2 doParallel_1.0.17
[10] rprojroot_2.0.4 processx_3.8.4 lattice_0.22-6
[13] MASS_7.3-60.0.1 crosstalk_1.2.1 dendextend_1.17.1
[16] backports_1.4.1 limma_3.58.1 sass_0.4.9
[19] rmarkdown_2.26 jquerylib_0.1.4 yaml_2.3.8
[22] httpuv_1.6.15 MsCoreUtils_1.14.1 minqa_1.2.6
[25] RColorBrewer_1.1-3 abind_1.4-5 zlibbioc_1.48.2
[28] AnnotationFilter_1.26.0 RCurl_1.98-1.14 seriation_1.5.5
[31] gdtools_0.3.7 GenomeInfoDbData_1.2.11 crul_1.4.2
[34] ncdf4_1.22 codetools_0.2-20 DelayedArray_0.28.0
[37] tidyselect_1.2.1 httpcode_0.3.0 farver_2.1.2
[40] lme4_1.1-35.3 TSP_1.2-4 webshot_0.5.5
[43] jsonlite_1.8.8 iterators_1.0.14 systemfonts_1.1.0
[46] foreach_1.5.2 tools_4.3.3 glue_1.7.0
[49] Rttf2pt1_1.3.12 SparseArray_1.2.4 BiocBaseUtils_1.4.0
[52] xfun_0.43 ca_0.71.1 withr_3.0.0
[55] BiocManager_1.30.23 fastmap_1.1.1 boot_1.3-30
[58] fansi_1.0.6 callr_3.7.6 digest_0.6.35
[61] timechange_0.3.0 R6_2.5.1 mime_0.12
[64] colorspace_2.1-0 utf8_1.2.4 generics_0.1.3
[67] fontLiberation_0.1.0 data.table_1.15.4 httr_1.4.7
[70] htmlwidgets_1.6.4 S4Arrays_1.2.1 pkgconfig_2.0.3
[73] gtable_0.3.5 registry_0.5-1 impute_1.76.0
[76] XVector_0.42.0 htmltools_0.5.8.1 fontBitstreamVera_0.1.1
[79] carData_3.0-5 MALDIquant_1.22.2 clue_0.3-65
[82] scales_1.3.0 knitr_1.46 tzdb_0.4.0
[85] nlme_3.1-164 curl_5.2.1 nloptr_2.0.3
[88] cachem_1.0.8 parallel_4.3.3 extrafont_0.19
[91] mzID_1.40.0 vsn_3.70.0 pillar_1.9.0
[94] grid_4.3.3 vctrs_0.6.5 pcaMethods_1.94.0
[97] promises_1.3.0 ggpubr_0.6.0 car_3.1-2
[100] xtable_1.8-4 cluster_2.1.6 extrafontdb_1.0
[103] evaluate_0.23 cli_3.6.2 compiler_4.3.3
[106] rlang_1.1.3 crayon_1.5.2 ggsignif_0.6.4
[109] labeling_0.4.3 ps_1.7.6 affy_1.80.0
[112] plyr_1.8.9 stringi_1.8.3 BiocParallel_1.36.0
[115] assertthat_0.2.1 munsell_0.5.1 lazyeval_0.2.2
[118] fontquiver_0.2.1 Matrix_1.6-5 hms_1.1.3
[121] gfonts_0.2.0 statmod_1.5.0 shiny_1.8.1.1
[124] igraph_2.0.3 broom_1.0.5 affyio_1.72.0
[127] bslib_0.7.0